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\large Salvatore Scellato
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\textbf{\large Summary}
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Online social networking services entice millions of users to spend
hours every day interacting with each other.
At the same time, thanks to the widespread and growing popularity of 
mobile devices equipped with location-sensing technology, users are now
increasingly sharing details about their geographic location and about the
places they visit. This adds a crucial spatial and geographic dimension to
online social services, bridging the gap between the online world and physical
presence. 

These observations motivate the work in this dissertation: our thesis is that
the spatial properties of online social networking services offer important
insights about users' social behaviour. This thesis is supported by a
set of results related to the measurement and the analysis of such spatial
properties.
%and by two case studies which discuss practical applications where
%such findings can be successfully exploited. 

First, we present a comparative study of three online social services: we
find that geographic distance constrains social connections, although users
exhibit heterogeneous spatial properties. Furthermore, we demonstrate that by
considering only
social or only spatial factors it is not possible to reproduce the observed
properties.  Therefore,
we investigate how these factors are jointly influencing the
evolution of online social services. The resulting observations are
then incorporated in a new model of network growth which is able to
reproduce the properties of real systems.

Then, we outline two case studies where we exploit our findings in 
real application scenarios.  The first concerns building a link prediction
system to find pairs of users likely to connect on online social services. Even
though spatial proximity fosters the creation of social ties, the computational
challenge is accurately and efficiently to discern when being close in space
results in a new social connection. We address this problem with a system that
uses, alongside other information, features based on the places that users
visit.  The second example presents a method to extract geographic information
about users sharing online videos to understand whether such videos are going to
become locally or globally popular. This information is then harnessed to build
caching policies that consider which items should be
prioritised in memory, thus improving performance of content delivery
networks.

We summarise our findings with a discussion about the implications of our
results, debating potential future research trends and practical applications.
